Consensus Fold Recognition by Predicted Model Quality

Abstract

Protein structure prediction has been a fundamental challenge in the biological field. In this post-genomic era, the need for automated protein structure prediction has never been more evident and researchers are now focusing on developing computational techniques to predict three-dimensional structures with high throughput. Consensus-based protein structure prediction methods are state-of-the-art in automatic protein structure prediction. A consensus-based server combines the outputs of several individual servers and tends to generate better predictions than any individual server. Consensus-based methods have proved to be successful in recent CASP (Critical Assessment of Structure Prediction). In this thesis, a Support Vector Machine (SVM) regression-based consensus method is proposed for protein fold recognition, a key component for high throughput protein structure prediction and protein function annotation. The SVM first extracts the features of a structural model by comparing the model to the other models produced by all the individual servers. Then, the SVM predicts the quality of each model. The experimental results from several LiveBench data sets confirm that our proposed consensus method, SVM regression, consistently performs better than any individual server. Based on this method, we developed a meta server, the Alignment by Consensus Estimation (ACE).